As computing systems have advanced, the use of computers in enterprises has increased significantly. The various parts of an enterprise each produce large amounts data in the normal course of conducting business operations. Analysis of such data may provide useful insights to improve or track business operations. Unfortunately, such data often is spread across a variety of disparate systems, such as the many different systems of an individual department. The large amounts of data and inherently different characteristics and formats of the data makes it complicated to properly analyze the data, especially in real-time scenarios.
Conventional solutions have involved developing custom analytic applications, which are individually tailored to each system. Such conventional solutions typically require many months to develop and also require a full team of dedicated developers. The development of the application, is thus, very time intensive and expensive. In addition, custom solutions often have performance and reliability issues, which limit the effectiveness of the solutions. In particular, the performance issues also can reduce the usefulness of attempts to provide real-time data. As a result, the long development periods and limited capabilities of the custom applications limit the benefits of the data analysis.
Thus, there exists a need to have more efficient development of analytics processes.